Electric motor drive systems (e.g., energy conversion systems including an electric machine) have achieved very high penetration in industrial, commercial and residential applications, where up to 89% of rotating machines use electric motors, while the remaining 11% are powered by combustion engines using fossil fuels (natural gas, gasoline, diesel, etc.). This makes electric motor drives the largest electricity consumers, accounting for almost 60% of industrial, up to 25% of commercial and up to 30% of residential electricity usage. Further, electric motor drive systems account for the largest electricity consumption in the U.S. and worldwide and are the workhorse of the industry due to their flexibility and reliability. Additionally, electric motor drive systems are extensively used in commercial and residential heating, ventilation and air conditioning (HVAC) systems in compressors, pumps and fans, modern transportation systems, and industrial manufacturing processes. With the electricity usage accounting for 97% of costs of electric motor drive operation and yearly electricity usage in GWh, even small improvements in the operating efficiency of the electric motor drive system, due to its market penetration, would result in billions of dollars in annual energy savings worldwide due to reduction in electrical energy usage and reduction of the burden on the environment by decreasing the demand for fossil fuels used to generate electricity
Additionally, due to high penetration and high electricity usage, electric motors and drives used in all applications are subject to new legislation which mandates minimum efficiency in order to promote energy conservation and release the stress on the power grid. Motor drive system efficiency is directly correlated to its losses, and, therefore it is important to well-characterize and model the combined motor drive efficiency. Losses such as mechanical friction, windage and stray losses in a machine or stray losses in an inverter, are often ignored or approximated since focus is given to major losses. Major losses include core and copper losses in a machine, and switching and conduction losses in an inverter. Such omissions create model inaccuracies and cause suboptimal hardware performance.
Currently, motor drive systems are modeled analytically using physics-based equations describing the phenomenon of electro-mechanical energy conversion which is governing the motor drive system. In particular, traditional modeling of electric motor drive systems is done mathematically where only the major losses in the motor and inverter are considered, due to the complexity of the system. However, the detailed analytical models of this phenomenon are hard to obtain due to their complexity and various nonlinearities and electrical, mechanical, thermal, and magnetic interactions in the motor drive. Therefore, analytical models are accurate to an extent but cannot include all possible power losses used in motor drive efficiency calculation. Inaccurate or incomplete analytical models that exist in the literature lead to inaccurate knowledge of a drive's efficiency and thus may not reflect accurate energy consumption.
Some electric motor drive losses are well understood and modeled either analytically with closed form solutions while others are accounted for using finite element method (FEM) based simulations. Methods of defining motor parameters on which loss models are based have well established testing procedures, although some of the losses are ignored or approximated due difficulty of modeling or their small magnitude.
In general, power losses in an induction motor drive system can be split into the following categories: (i) power conversion losses, which include semiconductor conduction and switching losses in rectifier, inverter, and other power electronics stages which are generally included in efficiency calculations, and stray electrical losses due to filtering or harmonics, and which are commonly ignored; and (ii) other losses in the motor drive system originate from the machine and can be divided into copper, iron or core, friction, windage and stray losses. Machine copper losses are generally included in efficiency models. Core losses are well understood but difficult to model and are often approximated in loss models with increasingly higher accuracy using analytical methods under both sinusoidal and pulse-width modulation (PWM) sources and FEM or other simulations. Friction, windage and stray losses are approximated in most drive-level efficiency models due to their complexity and hardware dependency. Thus, traditionally, as much as 10% of losses in the motor drive system are either approximated or simply ignored.
Accurately modeling electric motors and drives as a system is fundamental for motor drive efficiency analysis as well as control design, reliability testing, and fault diagnosis. Existing research has extensively studied individual induction machine and inverter models, including impact of inverter topologies and switching schemes on its losses, while research about the combined drive system efficiency is relatively rare. In such drive efficiency studies, certain power losses are ignored, assumed, or approximated as discussed above. In some studies, analysis is performed on very specific hardware setup and motor conditions which will not scale or transfer well. Further, the drive system can have different maximum efficiency operating points than the individual motor or inverter, making the derivation of the maximum efficiency point of the overall motor drive system a nontrivial task.
Based on existing literature, it is clear that loss analysis in a motor drive system for the purpose of efficiency modeling is a challenging task due to difficulty of modeling of electrical, magnetic, mechanical, and thermal interactions taking place in power electronics and the electric machine. Overall loss or efficiency models are highly dependent on the hardware used and are not easily adaptable.
Thus, a need exists for methods of generating a comprehensive model of the entire motor drive system that can be easily adapted to different hardware configurations, and combines the effects of the motor and inverter interaction with all inherent losses and non-idealities. Further, a need exists for methods of generating a comprehensive behavioral model based on physical measurements including all drive system losses that accurately estimate optimal points of performance of the motor drive system. These and other needs are addressed by the systems and methods of the present disclosure.